Whats the proper way of using Azure

from llama_index.core import Settings

Settings.llm = llm
Settings.embed_model = embed_model

%pip install llama-index-embeddings-azure-openai
%pip install llama-index-llms-azure-openai

!pip install llama-index
from llama_index.llms.azure_openai import AzureOpenAI
from llama_index.embeddings.azure_openai import AzureOpenAIEmbedding
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
import logging
import sys

logging.basicConfig(
    stream=sys.stdout, level=logging.INFO
)  # logging.DEBUG for more verbose output
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))

from google.colab import userdata

api_key = userdata.get("OPENAI1")
azure_endpoint = userdata.get("ENDPOINT")
api_version = "2023-07-01-preview"

llm = AzureOpenAI(
    model="gpt-4",
    deployment_name="my-deployment-name",
    api_key=api_key,
    azure_endpoint=azure_endpoint,
    api_version=api_version,
)

# You need to deploy your own embedding model as well as your own chat completion model
embed_model = AzureOpenAIEmbedding(
    model="text-embedding-ada-002",
    deployment_name="my-other-deployment-name",
    api_key=api_key,
    azure_endpoint=azure_endpoint,
    api_version=api_version,
)

from llama_index.core import (
    VectorStoreIndex,
    SummaryIndex,
    SimpleKeywordTableIndex,
    SimpleDirectoryReader,
    Settings,
)

Settings.llm = llm
Settings.embed_model = embed_model

!mkdir -p 'data/'
!wget -O "./data/paul_graham_essay.txt" "https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt"

documents = SimpleDirectoryReader(
    input_files=["./data/paul_graham_essay.txt"]
).load_data()
index = VectorStoreIndex.from_documents(documents)